Shen, JianbingPeng, JiantengDong, XingpingShao, LingPorikli, Fatih2024-05-061057-7149http://hdl.handle.net/1885/317311A novel energy minimization method for general higher-order binary energy functions is proposed in this paper. We first relax a discrete higher-order function to a continuous one, and use the Taylor expansion to obtain an approximate lower-order function, which is optimized by the quadratic pseudo-boolean optimization (QPBO) or other discrete optimizers. The minimum solution of this lower-order function is then used as a new local point, where we expand the original higher-order energy function again. Our algorithm does not restrict to any specific form of the higher-order binary function or bring in extra auxiliary variables. For concreteness, we show an application of segmentation with the appearance entropy, which is efficiently solved by our method. Experimental results demonstrate that our method outperforms state-of-the-art methodsThis work was supported in part by the National Basic Research Program of China (973 Program) under Grant 2013CB328805, in part by the National Natural Science Foundation of China under Grant 61272359, in part by the Australian Research Council’s Discovery Projects funding scheme under Grant DP150104645, and in part by the Fok Ying-Tong Education Foundation for Young Teachers. Specialized Fund for Joint Building Program of Beijing Municipal Education Commission.application/pdfen-AU© 2017 IEEE.Higher-order energyimage segmentation201710.1109/TIP.2017.27226912023-01-08